Techniques for providing visual translation cards including contextually relevant definitions and examples

ABSTRACT

Computer-implemented techniques can include receiving a selected word in a source language, obtaining one or more parts of speech for the selected word, and for each of the one or more parts-of-speech, obtaining candidate translations of the selected word to a different target language, each candidate translation corresponding to a particular semantic meaning of the selected word. The techniques can include for each semantic meaning of the selected word: obtaining an image corresponding to the semantic meaning of the selected word, and compiling translation information including (i) the semantic meaning, (ii) a corresponding part-of-speech, (iii) the image, and (iv) at least one corresponding candidate translation. The techniques can also include outputting the translation information.

FIELD

The present disclosure generally relates to language translation and,more particularly, to techniques for providing visual translation cardsincluding contextually relevant definitions and examples.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

When reading a document in a non-preferred language in a viewingapplication (a web browser, an e-reader, etc.), a user may periodicallycome across individual words that the user does not recognize orunderstand. In these events, the user may select a word in the documentto obtain its translation in one of his/her preferred languages. Thistypically involves cutting and pasting the selected word from thedocument into a search application or a language translationapplication. Any contextual information for the selected word istypically lost during this process. Because the selected word lacks anycontextual information, the user is presented with a plurality ofpossible translations of the selected word, many of which may becompletely inappropriate for the particular context of use of the word.

SUMMARY

A computer-implemented technique is presented. The technique can includereceiving, at a server having one or more processors, a selected word ina source language; obtaining, by the server, one or more parts of speechfor the selected word; for each of the one or more parts-of-speech,obtaining, by the server, candidate translations of the selected word toa different target language, each candidate translation corresponding toa particular semantic meaning of the selected word; for each semanticmeaning of the selected word: obtaining, by the server, an imagecorresponding to the semantic meaning of the selected word, andcompiling, by the server, translation information including (i) thesemantic meaning, (ii) a corresponding part-of-speech, (iii) the image,and (iv) at least one corresponding candidate translation; andoutputting, by the server, the translation information.

A computing system having one or more processors is also presented. Thecomputing system can be configured to perform operations comprising:receiving a selected word in a source language; obtaining one or moreparts of speech for the selected word; for each of the one or moreparts-of-speech, obtaining candidate translations of the selected wordto a different target language, each candidate translation correspondingto a particular semantic meaning of the selected word; for each semanticmeaning of the selected word: obtaining an image corresponding to thesemantic meaning of the selected word, and compiling translationinformation including (i) the semantic meaning, (ii) a correspondingpart-of-speech, (iii) the image, and (iv) at least one correspondingcandidate translation; and outputting the translation information.

In some implementations, for each semantic meaning of the selected word,an example sentence in the source language is obtained that includes aform of the selected word, wherein the translation information furtherincludes the example sentence. In some implementations, each examplesentence is descriptive of its corresponding image. In someimplementations, the selected word is a single word selected by a userfrom a document in the source language. In some implementations,obtaining the image corresponding to the selected words includesutilizing a machine-trained database of images associated with varioussemantic meanings.

In some implementations, for each semantic meaning of the selected word,a translation card is generated comprising the translation information,wherein outputting the translation information comprises outputting thetranslation cards. In some implementations, the selected word isreceived from a computing device associated with a user and receipt ofthe translation cards causes the computing device to display at leastone of the translation cards. In some implementations, the translationcards are grouped into sets of translation cards, wherein each set oftranslation cards corresponds to a different part-of-speech, and whereinreceipt of the translation cards causes the computing device to displayat least one of the translation cards from a particular set oftranslation cards.

In some implementations, receipt of the translation cards enables thecomputing device to both (i) transition between different translationcards of the particular set of translation cards, and (ii) transitionbetween different sets of translation cards. In some implementations,receipt of the translation cards enables the computing device to both(i) vertically transition between different translation cards of theparticular set of translation cards, and (ii) horizontally transitionbetween different sets of translation cards.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples areintended for purposes of illustration only and are not intended to limitthe scope of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description and the accompanying drawings, wherein:

FIG. 1 is a diagram of a computing system including an example serveraccording to some implementations of the present disclosure;

FIG. 2 is a functional block diagram of the example server of FIG. 1;and

FIGS. 3A-3C illustrate example verb translation cards according to someimplementations of the present disclosure;

FIGS. 4A-4B illustrate example verb translation cards displayed on acomputing device according to some implementations of the presentdisclosure;

FIG. 5 illustrates an example noun translation card according to someimplementations of the present disclosure;

FIG. 6 illustrates an example noun translation card displayed on thecomputing device according to some implementations of the presentdisclosure; and

FIG. 7 is a flow diagram of an example technique for providingtranslation cards including contextually relevant definitions andexamples according to some implementations of the present disclosure.

DETAILED DESCRIPTION

As previously mentioned, conventional language translation techniquesinvolve a user cutting and pasting a selected word from into a search ortranslation application, thereby losing any contextual information forthe selected word. For example, the word “bear” has a plurality ofmeanings in the English language. The word “bear” in English can meanthe animal, which is a noun, as well as a plurality of verb forms:carry, support, and turn or veer (in a direction). These conventionaltechniques present translations of these forms of the word “bear,”typically according to the frequency of use because there is nocontextual information. Thus, even though the context of the selectedword “bear” is “carry,” the user may be provided with a list oftranslations beginning with translations of the animal/noun form of“bear.” The user must then search through all of the potentialtranslations and attempt to discern the context. This process isdifficult and time consuming for the user.

Accordingly, techniques are presented for providing visual translationcards including contextually relevant definitions and examples. Eachvisual translation card may include (i) a sematic meaning of a selectedword in a source language, (ii) an image corresponding to the selectedword and obtained using its semantic meaning, (iii) a correspondingpart-of-speech and (iv) at least one corresponding candidate translationof the selected word to a target language. These techniques provideusers the ability to quickly ascertain an appropriate translation of aselected word through the use of images. In some implementations, thevisual translation cards can include example sentences having a form ofthe selected word and that are descriptive of the displayed images,thereby providing contextually relevant, more useful information. A userinterface can also provide users the ability to transition betweendifferent visual translation cards for a particular part-of-speech(e.g., verbs) and to transition between different visual translationcards for different parts-of-speech (e.g., transition from verbtranslation cards to noun translation cards).

Referring now to FIG. 1, a computing system 100 including an exampleserver 104 is illustrated. The term “server” as used herein can refer toboth a single computing device and a plurality of computing devices(e.g., a computing system) operating in a parallel or distributedarchitecture. The server 104 can communicate with a computing device 108via a network 112. Examples of the computing device 108 include, but arenot limited to, a desktop computer, a laptop computer, a tabletcomputer, and a mobile phone. The network 112 can include a local areanetwork (LAN), a wide area network (WAN), e.g., the Internet, or acombination thereof. A user 116 can interact with the computing device108, such as to provide a selected word for translation and to viewtranslation information for the selected word.

Referring now to FIG. 2, the example server 104 is illustrated. Theserver 104 can include a communication device 200, a processor 204, anda memory 208. The communication device 200 can include any suitablecomponents (e.g., a transceiver) configured for communication with thecomputing device 108. The memory 208 can be any suitable storage medium(flash, hard disk, etc.) configured to store information at the server104. For example, the memory 208 may store a machine-trained databaserelating images to various semantic meanings. The processor 204 cancontrol operation of the server 104. Example functions performed by theprocessor 208 include, but are not limited to, loading/executing anoperating system of the computing device 104, controlling communicationwith other components on the network 112 (e.g., the computing device108) via the communication device 200, and controlling read/writeoperations at the memory 208. The term “processor” as used herein canrefer to both a single processor and a plurality of processors operatingin a parallel or distributed architecture. The processor 204 can also beconfigured to perform at least a portion of the techniques of thepresent disclosure, which are now discussed in greater detail.

In the example scenario below, the user 116 has selected the word “bear”in the English (source) language used in the context of “to carry” andis looking for a translation of the selected word to the Spanish(target) language. For example, the user 116 may be a native Spanishspeaker and is unsure of the meaning of the word “bear” in an Englishlanguage document, such as a web page. Referring now to FIGS. 3A-3C,example verb translation cards 300 a, 300 b, 300 c (“translation cards300”) are illustrated. A single header 304 is indicative of thepart-of-speech for each of the translation cards 300. This configurationhaving multiple translation cards concurrently displayed could beutilized, for example, when the computing device 108 is a desktop,laptop, or tablet computer having a relatively large display 120.

In FIG. 3A, the translation card 300 a includes (i) a semantic meaning308 (“carry”) of the selected word (“bear”) and, in someimplementations, a relative frequency indicator 312 indicative of afrequency of occurrence of the semantic meaning 308. The translationcard 300 a can also include images 320 a, 320 b (“images 320”) obtainedusing the semantic meaning 308 and corresponding to the selected word.As shown, each image 320 illustrates a waiter carrying a tray ofglasses. These images 320, for example, may be obtained using amachine-trained database relating images to various semantic meanings.The translation card 300 a can also include an example sentence 316having a form of the selected word (“bearing”) and that is descriptiveof the images 320. As shown, the example sentence 316 is “he was bearinga tray of brimming glasses.” In some implementations, the translationcard 300 a can further include a list of synonyms 324 for the semanticmeaning 308. As shown, the list of synonyms 324 includes bring,transport, move, and convey.

The translation card 300 a can further include or can otherwise beassociated with selectable icons 328, 332, and 336. These selectableicons 328, 332, 336 can be used to cycle between different sub-meaningsof the semantic meaning 308. As shown, the word “carry” has threedifferent sub-meanings—to carry an object, to convey passengers orcargo, and to display a visible mark or feature. Each of thesesub-meanings can have a different translation in Spanish. The displayedtranslation card 300 a is associated with the sub-meaning “to carry anobject” and thus selectable icon 328 is highlighted and candidatetranslations (Ilevar, portar) are shown. If the user 116 were to selectanother one of the selectable icons 332 or 336 associated with differentsub-meanings, the appearance of the translation card 300 a would change.More particularly, the example sentence, image(s), list of synonyms, andcandidate translation(s) would change to reflect the differentsub-meaning.

In FIG. 3B, the translation card 300 b is associated with the semanticmeaning “support” as shown at 344. Images 352 a, 352 b (“images 352”)illustrate humans supporting the weight of a large rock. An examplesentence 348 includes a form of the selected word (“bear”) and isdescriptive of the images 352 (“he could no longer bear the weight ofthe rock”). A list of synonyms 356 includes the words carry, hold up,and prop up. A highlighted selectable icon 360 is associated with thesub-meaning “to support an object” and displays the candidatetranslation “sustener” in Spanish. Other selectable icons 364, 368 areassociated with the sub-meanings “to take responsibility for” and “to beable to accept or stand up to.”

In FIG. 3C, the translation card 300 c is associated with the semanticmeaning “turn (in a direction)” as shown at 374. Image 382 illustrates afork in a path through the woods. An example sentence 378 includes aform of the selected word (“bear”) and is descriptive of the image 382(“bear right and follow the old road”). A list of synonyms 386 includesthe words veer, curve, swerve, and fork. A highlighted selectable icon390 is associated with the sub-meaning “to turn in a direction” anddisplays the candidate translation “mentener” in Spanish. Otherselectable icons 394, 398 are associated with the sub-meanings “tomaintain” and “to keep.”

Referring now to FIGS. 4A-4B, single translation card displays areillustrated. This configuration having only a single translation carddisplayed at a time could be utilized, for example, when the computingdevice 108 is a mobile phone or tablet computer having a relativelysmall display 120. A header 400 is indicative of the part-of-speech andthe user 116 can horizontally scroll to a different part-of-speech(e.g., from verb to noun). In FIG. 4A, translation card 300 a isdisplayed. In the illustrated example, the translation card 300 a canfurther include a translation 404 of the example sentence 316 to Spanish(“Él estaba Ilevando una bandeja con copas rebosantes”) with the words“bearing” and “Ilevando” both emphasized. Towards a bottom of thetranslation card 300 a, the user 116 can vertically scroll between thedifferent sub-meanings of the semantic meaning “carry.”

In FIG. 4B, translation card 300 b is displayed. In the illustratedexample, the translation card 300 b can further include a translation408 of the example sentence 348 to Spanish (“Que ya no podia soportar elpeso de la roca”) with the words “bear” and “soportar” both emphasized.Towards a bottom of the translation card 300 b, the user 116 canvertically scroll between the different sub-meanings of the semanticmeaning “support.”

Referring now to FIG. 5, an example noun translation card 500 isillustrated. A single header 504 is indicative of the part-of-speech forthe translation card 500. As shown, the translation card 500 includesthree portions 502 a, 502 b, 502 c (“portions 502”), each portion 502displaying different information. This configuration having multipleportions for a single translation card concurrently displayed could beutilized, for example, when the computing device 108 is a desktop,laptop, or tablet computer having a relatively large display 120.

The first portion 502 a of the translation card 500 includes a semanticmeaning 508 (“bear,” the animal) of the selected word (“bear”), anexample sentence including a form of the selected word (“bear”), and alist of synonyms 520 (bruin, grizzly (bear), black bear, brown bear,etc.). The second portion 502 b of the translation card 500 can includea candidate translation 524 in Spanish (“el oso”) and a relativefrequency indicator 528. The third portion 502 c of the translation card500 can include a tree 532 of most popular related words to the selectedword (“bear”). As shown, these related words include polar, grizzly,black, brown, teddy, and Russian, which are each types of the animal, aswell as non-animal related words including markets, hunt, and hug. Thetranslation card 500 can further include one or more images. As shown,the translation card 500 includes three sets of images 536, 540, 544associated with the first, second, and third portions 502 a, 502 b, 502c, respectively. The first set of images 536 is of polar bears, thesecond set of images 540 is of black bears, and the third set of images544 is of grizzly bears. All of these images, as well as their divisioninto sets, also relates to the example sentence: “Multiple bear specieshave been crossbred in zoos” (i.e., polar, black, and grizzly are each aspecies of bear).

Referring now to FIG. 6, a single translation card display isillustrated. This configuration having only a single translation carddisplayed at a time could be utilized, for example, when the computingdevice 108 is a mobile phone or tablet computer having a relativelysmall display 120. A header 600 is indicative of the part-of-speech andthe user 116 can horizontally scroll to a different part-of-speech(e.g., from noun to verb). As shown, a portion of the translation card500 is displayed. In the illustrated example, the translation card 500can further include a translation 604 of the example sentence 516 toSpanish (“Varias especies de osos se han cruzado en los zoológicos”)with the words “bear” and “osos” both emphasized. The displayed portionof the translation card 500 may also include fewer images (e.g., oneimage of a polar bear and one image of a black bear), but the displayedimages still relate to the example sentence 516.

Referring now to FIG. 7, a flow diagram of an example technique 700 forproviding visual translation cards including contextually relevantdefinitions and examples is illustrated. At 704, the server 104 canreceive a selected word in a source language. At 708, the server canobtain one or more parts of speech for the selected word. At 712, theserver 104 can, for each of the one or more parts-of-speech, obtaincandidate translations of the selected word to a different targetlanguage, each candidate translation corresponding to a particularsemantic meaning of the selected word. At 716, the server 104 can, foreach semantic meaning of the selected word, obtain an imagecorresponding to the semantic meaning of the selected word. At 720, theserver 104 can, for each semantic meaning of the selected word, compiletranslation information including (i) the semantic meaning, (ii) acorresponding part-of-speech, (iii) the image, and (iv) at least onecorresponding candidate translation. At 724, the server 104 can outputthe translation information, e.g., to the computing device 108. Thetechnique 700 can then end or return to 704.

Example embodiments are provided so that this disclosure will bethorough, and will fully convey the scope to those who are skilled inthe art. Numerous specific details are set forth such as examples ofspecific components, devices, and methods, to provide a thoroughunderstanding of embodiments of the present disclosure. It will beapparent to those skilled in the art that specific details need not beemployed, that example embodiments may be embodied in many differentforms and that neither should be construed to limit the scope of thedisclosure. In some example embodiments, well-known procedures,well-known device structures, and well-known technologies are notdescribed in detail.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The term “and/or” includes any and all combinations of one ormore of the associated listed items. The terms “comprises,”“comprising,” “including,” and “having,” are inclusive and thereforespecify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. The method steps,processes, and operations described herein are not to be construed asnecessarily requiring their performance in the particular orderdiscussed or illustrated, unless specifically identified as an order ofperformance. It is also to be understood that additional or alternativesteps may be employed.

Although the terms first, second, third, etc. may be used herein todescribe various elements, components, regions, layers and/or sections,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer or section from another region,layer or section. Terms such as “first,” “second,” and other numericalterms when used herein do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer or section discussed below could be termed a second element,component, region, layer or section without departing from the teachingsof the example embodiments.

As used herein, the term module may refer to, be part of, or include: anApplication Specific Integrated Circuit (ASIC); an electronic circuit; acombinational logic circuit; a field programmable gate array (FPGA); aprocessor or a distributed network of processors (shared, dedicated, orgrouped) and storage in networked clusters or datacenters that executescode or a process; other suitable components that provide the describedfunctionality; or a combination of some or all of the above, such as ina system-on-chip. The term module may also include memory (shared,dedicated, or grouped) that stores code executed by the one or moreprocessors.

The term code, as used above, may include software, firmware, byte-codeand/or microcode, and may refer to programs, routines, functions,classes, and/or objects. The term shared, as used above, means that someor all code from multiple modules may be executed using a single(shared) processor. In addition, some or all code from multiple modulesmay be stored by a single (shared) memory. The term group, as usedabove, means that some or all code from a single module may be executedusing a group of processors. In addition, some or all code from a singlemodule may be stored using a group of memories.

The techniques described herein may be implemented by one or morecomputer programs executed by one or more processors. The computerprograms include processor-executable instructions that are stored on anon-transitory tangible computer readable medium. The computer programsmay also include stored data. Non-limiting examples of thenon-transitory tangible computer readable medium are nonvolatile memory,magnetic storage, and optical storage.

Some portions of the above description present the techniques describedherein in terms of algorithms and symbolic representations of operationson information. These algorithmic descriptions and representations arethe means used by those skilled in the data processing arts to mosteffectively convey the substance of their work to others skilled in theart. These operations, while described functionally or logically, areunderstood to be implemented by computer programs. Furthermore, it hasalso proven convenient at times to refer to these arrangements ofoperations as modules or by functional names, without loss ofgenerality.

Unless specifically stated otherwise as apparent from the abovediscussion, it is appreciated that throughout the description,discussions utilizing terms such as “processing” or “computing” or“calculating” or “determining” or “displaying” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

Certain aspects of the described techniques include process steps andinstructions described herein in the form of an algorithm. It should benoted that the described process steps and instructions could beembodied in software, firmware or hardware, and when embodied insoftware, could be downloaded to reside on and be operated fromdifferent platforms used by real time network operating systems.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer. Such acomputer program may be stored in a tangible computer readable storagemedium, such as, but is not limited to, any type of disk includingfloppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-onlymemories (ROMs), random access memories (RAMs), EPROMs, EEPROMs,magnetic or optical cards, application specific integrated circuits(ASICs), or any type of media suitable for storing electronicinstructions, and each coupled to a computer system bus. Furthermore,the computers referred to in the specification may include a singleprocessor or may be architectures employing multiple processor designsfor increased computing capability.

The algorithms and operations presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may also be used with programs in accordancewith the teachings herein, or it may prove convenient to construct morespecialized apparatuses to perform the required method steps. Therequired structure for a variety of these systems will be apparent tothose of skill in the art, along with equivalent variations. Inaddition, the present disclosure is not described with reference to anyparticular programming language. It is appreciated that a variety ofprogramming languages may be used to implement the teachings of thepresent disclosure as described herein, and any references to specificlanguages are provided for disclosure of enablement and best mode of thepresent invention.

The present disclosure is well suited to a wide variety of computernetwork systems over numerous topologies. Within this field, theconfiguration and management of large networks comprise storage devicesand computers that are communicatively coupled to dissimilar computersand storage devices over a network, such as the Internet.

The foregoing description of the embodiments has been provided forpurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure. Individual elements or featuresof a particular embodiment are generally not limited to that particularembodiment, but, where applicable, are interchangeable and can be usedin a selected embodiment, even if not specifically shown or described.The same may also be varied in many ways. Such variations are not to beregarded as a departure from the disclosure, and all such modificationsare intended to be included within the scope of the disclosure.

What is claimed is:
 1. A computer-implemented method, comprising:receiving, at a server having one or more processors and from acomputing device via a network, a selected word in a source language,the computing device being associated with a user; obtaining, by theserver, parts-of-speech for the selected word; for each of theparts-of-speech for the selected word: (i) obtaining, by the server,candidate translations of the selected word to a different targetlanguage, each candidate translation corresponding to a particularsemantic meaning of a plurality of semantic meanings of the selectedword; and (ii) for each particular semantic meaning of the selectedword: (a) obtaining, by the server, an image corresponding to theparticular semantic meaning of the selected word by utilizing amachine-trained database of images associated with various semanticmeanings, (b) obtaining an example sentence in the source language thatincludes a form of the selected word and is descriptive of a context ofthe corresponding image, (c) compiling, by the server, translationinformation including (i) the particular semantic meaning, (ii) acorresponding part-of-speech, (iii) the image, (iv) the examplesentence, and (v) at least one corresponding candidate translation, and(d) generating, by the server, a translation card or an instruction togenerate the translation card, the translation card comprising thetranslation information; and outputting, by the server and to thecomputing device via the network, the plurality of translation cards orthe plurality of instructions, wherein receipt of the plurality oftranslation cards or the plurality of instructions causes the computingdevice to: render at least one of a first set of the plurality oftranslation cards on a display of the computing device, the first set ofthe plurality of translation cards including at least one translationcard for a first part-of-speech of the parts-of-speech, each translationcard of the first set of the plurality of translation cardscorresponding to a different semantic meaning of the selected word; andrender a first selectable icon on the display, the first selectable iconbeing configured to, upon its selection, render at least one of adifferent second set of the plurality of translation cards on thedisplay, the second set of the plurality of translation cards includingat least one translation card for a second part-of-speech of theparts-of-speech.
 2. The computer-implemented method of claim 1, whereinthe selected word is a single word selected by a user from a document inthe source language.
 3. The computer-implemented method of claim 1,further comprising for each semantic meaning of the selected word,obtaining a relative frequency of occurrence of the semantic meaning,wherein the translation information includes a relative frequencyindicator indicative of the relative frequency of occurrence.
 4. Thecomputer-implemented method of claim 1, wherein receipt of the pluralityof translation cards or the plurality of instructions causes thecomputing device to concurrently render two or more of the first set ofthe plurality of translation cards on the display.
 5. Thecomputer-implemented method of claim 1, wherein receipt of the pluralityof translation cards or the plurality of instructions further causes thecomputing device to: render a second selectable icon on the display, thesecond selectable icon being configured to, upon its selection, renderanother one of the first set of the plurality of translation cards onthe display.
 6. The computer-implemented method of claim 5, wherein theother one of the first set of the plurality of translation cardscorresponds to a different semantic meaning of the selected word.
 7. Thecomputer-implemented method of claim 5, wherein the other one of thefirst set of the plurality of translation cards corresponds to adifferent semantic sub-meaning of a particular semantic meaning of thetranslation card that was initially rendered.
 8. A computing systemhaving one or more processors configured to perform operationscomprising: receiving, from a computing device via a network, a selectedword in a source language, the computing device being associated with auser; obtaining parts-of-speech for the selected word; for each of theparts-of-speech of the selected word: (i) obtaining candidatetranslations of the selected word to a different target language, eachcandidate translation corresponding to a particular semantic meaning ofa plurality of semantic meanings of the selected word; and (ii) for eachparticular semantic meaning of the selected word: (a) obtaining an imagecorresponding to the particular semantic meaning of the selected word byutilizing a machine-trained database of images associated with varioussemantic meanings, (b) obtaining an example sentence in the sourcelanguage that includes a form of the selected word and is descriptive ofa context of the corresponding image, (c) compiling translationinformation including (i) the semantic meaning, (ii) a correspondingpart-of-speech, (iii) the image, (iv) the example sentence, and (v) atleast one corresponding candidate translation, and (d) generating atranslation card or an instruction to generate the translation card, thetranslation card comprising the translation information; and outputting,to the computing device via the network, the plurality of translationcards or the plurality of instructions, wherein receipt of the pluralityof translation cards or the plurality of instructions causes thecomputing device to: render at least one of a first set of the pluralityof the translation cards on a display of the computing device, the firstset of the plurality of translation cards including at least onetranslation card for a first part-of-speech of the parts-of-speech, eachtranslation card of the first set of the plurality of translation cardscorresponding to a different semantic meaning of the selected word, andrender a first selectable icon on the display, the first selectable iconbeing configured to, upon its selection, render at least one of adifferent second set of the plurality of translation cards on thedisplay, the second set of the plurality of translation cards includingat least one translation card for a second part-of-speech of theparts-of-speech.
 9. The computing system of claim 8, wherein theselected word is a single word selected by a user from a document in thesource language.
 10. The computing system of claim 8, wherein theoperations further comprise for each semantic meaning of the selectedword, obtaining a relative frequency of occurrence of the semanticmeaning, wherein the translation information includes a relativefrequency indicator indicative of the relative frequency of occurrence.11. The computing system of claim 8, wherein receipt of the plurality oftranslation cards or the plurality of instructions causes the computingdevice to concurrently render two or more of the first set of theplurality of translation cards on the display.
 12. The computing systemof claim 8, wherein receipt of the plurality of translation cards or theplurality of instructions further causes the computing device to: rendera second selectable icon on the display, the second selectable iconbeing configured to, upon its selection, render another one of the firstset of the plurality of translation cards on the display.
 13. Thecomputing system of claim 12, wherein the other one of the first set ofthe plurality of translation cards corresponds to a different semanticmeaning of the selected word.
 14. The computing system of claim 12,wherein the other one of the first set of the plurality of translationcards corresponds to a different semantic sub-meaning of a particularsemantic meaning of the translation card that was initially rendered.